Merging and Annotating Teeth and Roots from Automated Segmentation of Multimodal Images

Deleat‐Besson, Romain; Le, Celia; Zhang, Winston; Turkestani, Najla Al; Cevidanes, Lúcia Helena Soares; Bianchi, Jonas; Ruellas, Antônio Carlos de Oliveira; Gurgel, Marcela; Massaro, Camila; Castillo, Aron Aliaga–Del; Ioshida, Marcos; Yatabe, Marília; Benavides, Erika; Ríos, Héctor F.; Soki, Fabiana N.; Neiva, Gisele F.; Najarian, Kayvan; Gryak, Jonathan; Styner, Martin; Aristizabal, Juan Fernando; Rey, Diego; Álvarez, María Antonia; Bert, Loris; Soroushmehr, S. M. Reza; Prieto, Juan Carlos

doi:10.1007/978-3-030-89847-2_8

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Some investigators have used the idea of multimodality to process the 2D slice image and intraoral scan model of Cone-Beam Computed Tomography (CBCT) separately, and merge the processing results to complete tooth segmentation (Deleat-Besson et al, 2021). Some researchers designed a graph attention convolutional layer structure and a global branching structure to complete the local and global feature extraction of the dental model, respectively (Zhao et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

An Efficient Method of Tooth Segmentation Under Massive Medical Data

Yang

et al. 2022

Journal of Database Management

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To accurately and efficiently complete tooth segmentation from a large amount of oral medical data, the burden of doctors should be reduced. An automatic seed picking method based on 2D projection of the occlusal plane was proposed. First, the authors establish a two-dimensional seed data set for tooth segmentation. Then, this article built a prediction network of teeth seeds based on YOLOv4 to realize the prediction of teeth position as well as the recognition of teeth categories. Finally, according to the statistical optimal seed position, the two-dimensional seeds are calculated and mapped back to the three-dimensional space by the reverse projection transformation method to realize the final picking up of the three-dimensional seeds. Furthermore, combined with the previous work of division line detection, the automatic segmentation of the 3D dental model was realized. The experimental results show that the proposed method has high accuracy and real-time performance, which significantly reduces the burden of human-computer interaction in dental model segmentation.

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Section: Related Workmentioning

confidence: 99%